Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets
Summary
A systematic review of non-social media free-text datasets for mental health disorder detection, identifying biases and gaps in current resources.
View Cached Full Text
Cached at: 07/07/26, 04:37 AM
# Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets Source: [https://arxiv.org/abs/2607.03540](https://arxiv.org/abs/2607.03540) [View PDF](https://arxiv.org/pdf/2607.03540) > Abstract:Detecting mental health disorders in a timely manner is an important societal challenge\. NLP and machine learning \(ML\) methods used to assist with detection rely on data collected primarily from social media\. However, such datasets often have sampling biases and inherent ethical and privacy issues\. One avenue to overcome these limitations is non\-social media data\. We present the first comprehensive review of non\-social media, free\-text datasets for mental health research\. We use the PRISMA methodology to conduct our survey and we review datasets available in multiple languages\. We find that non\-social media free\-text based datasets are predominantly focused on English and on detecting depression\. These datasets also vary in demographics, platforms, data types, annotation techniques, and methodologies\. This systematic review also reveals key gaps and highlights opportunities to develop more diverse, reliable and clinically\-relevant resources\. ## Submission history From: Sadiya Sayara Chowdhury Puspo \[[view email](https://arxiv.org/show-email/f079a157/2607.03540)\] **\[v1\]**Fri, 3 Jul 2026 18:00:17 UTC \(7,165 KB\)
Similar Articles
Depression Risk Assessment in Social Media via Large Language Models
Researchers present a zero-shot LLM system that assesses depression risk from Reddit posts, achieving competitive F1 scores and demonstrating scalable mental-health monitoring.
Population-Level Profiling of DSM-5 Depressive Symptoms Among Self-Reported ADHD and ASD Users on Twitter: An Exploratory Study Using Advanced NLP and Statistical Analysis
This study analyzes tweets from self-reported ADHD and ASD users to profile DSM-5 depressive symptoms using NLP, finding population-level differences in symptom expression between the two groups despite limited classification performance.
Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
This paper presents an LLM-based pipeline for analyzing mental health changes from sequentially ordered social media posts, participating in the CLPsych 2026 shared task. It performs post-level assessment and user-level temporal modeling to capture shifts in psychological well-being.
psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis
This paper describes the use of NLP methods including LSTM, BERT, and LLMs for analyzing mental health states from social media posts as part of the CLPsych 2026 shared task, achieving top consistency scores for summarization.
MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection
This paper presents MentalMARBERT, a domain-adapted Arabic language model for detecting mental health disorders from social media text. The framework uses domain-adaptive pre-training and a two-stage fine-tuning approach, achieving 0.877 accuracy and 0.861 macro-F1 on a newly constructed Arabic mental health dataset of 50,670 tweets.